SOTAVerified

Multi-Label Classification

multilabel graph classification with highest result

Papers

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Showing 876–900 of 1198 papers

TitleStatusHype
Seq2Emo for Multi-label Emotion Classification Based on Latent Variable Chains Transformation—0
Joint Ranking SVM and Binary Relevance with Robust Low-Rank Learning for Multi-Label ClassificationCode0
A Scalable Multilabel Classification to Deploy Deep Learning Architectures For Edge Devices—0
Recognizing Conflict Opinions in Aspect-level Sentiment Classification with Dual Attention Networks—0
Weakly Supervised Attention Networks for Entity Recognition—0
A new GAN-based anomaly detection (GBAD) approach for multi-threat object classification on large-scale x-ray security images—0
Collaborative Graph Walk for Semi-supervised Multi-Label Node ClassificationCode0
A Multilabel Classification Framework for Approximate Nearest Neighbor SearchCode0
Multi Label Restricted Boltzmann Machine for Non-Intrusive Load Monitoring—0
Using Supervised Learning to Classify Metadata of Research Data by Discipline of Research—0
A multi-label, dual-output deep neural network for automated bug triaging—0
Multi-label Categorization of Accounts of Sexism using a Neural FrameworkCode0
Label-PEnet: Sequential Label Propagation and Enhancement Networks for Weakly Supervised Instance Segmentation—0
Learning Category Correlations for Multi-label Image Recognition with Graph Networks—0
A Self-consistent-field Iteration for Orthogonal Canonical Correlation Analysis—0
End-to-End Neural Speaker Diarization with Permutation-Free Objectives—0
DyANE: Dynamics-aware node embedding for temporal networks—0
Privacy-Preserving BanditsCode0
Learning to Learn and Predict: A Meta-Learning Approach for Multi-Label Classification—0
Order-free Learning Alleviating Exposure Bias in Multi-label ClassificationCode0
Student Performance Prediction with Optimum Multilabel Ensemble Model—0
Parity Partition Coding for Sharp Multi-Label Classification—0
Efficient Discovery of Expressive Multi-label Rules using Relaxed PruningCode0
On the Trade-off Between Consistency and Coverage in Multi-label Rule Learning HeuristicsCode0
Multi-label Classification for Fault Diagnosis of Rotating Electrical Machines—0
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All datasetsCheXpertMS-COCOPASCAL VOC 2007NUS-WIDEChestX-ray14OpenImages-v6PASCAL VOC 2012MLRSNetMRNetMIMIC-CXR

Benchmark Results

#ModelMetricClaimedVerifiedStatus
1CFT (ensemble) Macao Polytechnic UniversityAVERAGE AUC ON 14 LABEL0.93—Unverified
2Hierarchical-Learning-V1 (ensemble)AVERAGE AUC ON 14 LABEL0.93—Unverified
3DeepAUC-v1AVERAGE AUC ON 14 LABEL0.93—Unverified
4Conditional-Training-LSRAVERAGE AUC ON 14 LABEL0.93—Unverified
5Hierarchical-Learning-V4 (ensemble)AVERAGE AUC ON 14 LABEL0.93—Unverified
6YWW(ensemble)AVERAGE AUC ON 14 LABEL0.93—Unverified
7Hierarchical-Learning-V0 (ensemble)AVERAGE AUC ON 14 LABEL0.93—Unverified
8Conditional-Training-LSR-V1AVERAGE AUC ON 14 LABEL0.93—Unverified
9DeepCNNsGM(ensemble)AVERAGE AUC ON 14 LABEL0.93—Unverified
10Multi-Stage-Learning-CNN-V3 (ensemble)AVERAGE AUC ON 14 LABEL0.93—Unverified